STEM与日常科技·英语30篇（1）
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How Speech Recognition Turns Sound into Text
语音识别如何把声音变文字
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Microphones convert sound waves into electrical signals, which analog-to-digital converters sample thousands of times per second.
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Acoustic models break audio into tiny frames — typically 10-millisecond windows — and classify each frame’s phonetic likelihood using neural networks.
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Language models predict probable word sequences based on grammar, context, and vast text corpora, helping resolve ambiguities like 'there' vs. 'their'.
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Speaker adaptation techniques adjust recognition for individual voices, accents, or background noise using recent utterances.
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End-to-end systems now skip intermediate steps, mapping raw audio directly to text with attention-based transformers trained on millions of hours of speech.
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Real-time transcription requires buffering short segments, predicting words before full sentences finish, and correcting errors on-the-fly.
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Privacy-conscious devices process voice locally whenever possible, sending only anonymized snippets to servers for improvement.
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Accuracy exceeds 95% in quiet settings but drops significantly with overlapping speakers, heavy accents, or technical jargon without custom training.